Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "184" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 32 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 30 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459848 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 30.15% | 0.50% | -0.059707 | -0.971000 | -0.064717 | -0.515830 | 0.617059 | -1.148509 | -0.153974 | -1.059628 | 0.7293 | 0.7517 | 0.3719 | 1.623455 | 1.520691 |
| 2459846 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 33.33% | 0.00% | -0.333441 | -1.336173 | 0.868488 | -0.568646 | 0.573825 | -0.268417 | -0.238041 | -0.833286 | 0.8300 | 0.6611 | 0.5063 | 2.083489 | 1.547236 |
| 2459845 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 25.41% | 74.59% | 0.330814 | -1.012410 | -0.014710 | -0.924981 | 0.439540 | -1.377093 | 0.664242 | -1.382901 | 0.7285 | 0.7379 | 0.3855 | 1.220933 | 1.810715 |
| 2459844 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.953217 | -0.477631 | 0.643951 | 0.516115 | 2.083557 | -0.056970 | 1.110164 | 0.677131 | 0.0246 | 0.0242 | 0.0005 | nan | nan |
| 2459843 | digital_ok | 0.00% | 1.20% | 0.66% | 0.00% | 15.22% | 0.00% | -1.078625 | -1.317854 | -0.569131 | 1.195869 | -0.893498 | 0.336879 | -0.384383 | -0.960001 | 0.7429 | 0.7360 | 0.4007 | 1.715503 | 1.679793 |
| 2459842 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.048975 | -0.616074 | 0.342707 | 1.442299 | 0.464027 | 0.260036 | 0.006005 | 0.044363 | 0.7686 | 0.6788 | 0.2599 | 2.397219 | 2.207056 |
| 2459841 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | -0.077259 | 1.063557 | 0.505100 | 1.391335 | 5.619375 | -1.189353 | 2.009804 | 2.019914 | 0.0247 | 0.0240 | 0.0007 | nan | nan |
| 2459840 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 233.239785 | 638.478181 | 129.755054 | 248.266629 | 1685.360470 | 4190.869258 | 3873.667600 | 10738.583623 | 0.0169 | 0.0120 | 0.0057 | nan | nan |
| 2459839 | digital_ok | 0.00% | - | - | - | - | - | -0.847477 | 0.108064 | 0.929016 | 0.154366 | 0.698629 | 2.546842 | 1.436278 | 0.949913 | nan | nan | nan | nan | nan |
| 2459838 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 1.22% | 1.014209 | -0.635973 | -0.001290 | 0.394995 | 0.275001 | 0.062469 | 0.769355 | -0.274101 | 0.7579 | 0.7132 | 0.3979 | 2.436502 | 2.066359 |
| 2459836 | digital_ok | - | 100.00% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0328 | 0.0369 | 0.0027 | nan | nan |
| 2459835 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | -1.050764 | -1.113363 | -1.135698 | -0.932414 | 13.643366 | 10.528144 | 41.343540 | 41.729002 | 0.0335 | 0.0354 | 0.0015 | nan | nan |
| 2459833 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 1.211034 | 0.622250 | -0.438841 | -0.258315 | 16.100960 | 14.736795 | 42.717252 | 44.470719 | 0.0275 | 0.0292 | 0.0004 | nan | nan |
| 2459832 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -0.004625 | -0.512906 | -0.110757 | 0.142352 | -0.911517 | -0.628563 | 0.793736 | -0.719936 | 0.8064 | 0.5432 | 0.5773 | 2.016228 | 1.691505 |
| 2459831 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.598583 | 0.068324 | 0.771229 | 0.156007 | -0.055063 | 1.554546 | 1.043641 | 0.861280 | 0.0259 | 0.0276 | 0.0029 | nan | nan |
| 2459830 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459829 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.65% | 0.00% | 0.752784 | -0.615372 | 0.027876 | 0.787924 | 0.650322 | -0.275879 | 2.891889 | -0.269991 | 0.7592 | 0.6781 | 0.4096 | 0.839409 | 0.648226 |
| 2459828 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459827 | digital_ok | 100.00% | 2.15% | 0.00% | 0.00% | 100.00% | 0.00% | 0.658878 | 0.108232 | 2.038865 | 0.607006 | 44.329740 | -0.232618 | 6.968506 | -1.186698 | 0.7485 | 0.6911 | 0.4145 | 0.000000 | 0.000000 |
| 2459826 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 84.21% | 2.63% | 0.161540 | 0.049882 | -0.124282 | 0.355315 | -0.113056 | 0.461673 | 2.088135 | -0.592594 | 0.8048 | 0.5946 | 0.5079 | 0.000000 | 0.000000 |
| 2459825 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.307998 | -0.132668 | 0.486581 | 0.589347 | 6.144993 | 6.249628 | 7.712168 | 5.727910 | 0.8005 | 0.6081 | 0.5035 | 5.778522 | 5.262750 |
| 2459824 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.131042 | -0.415589 | 0.747069 | 0.719033 | 4.702924 | 4.225747 | 6.845240 | 5.561031 | 0.7370 | 0.7416 | 0.3552 | 5.342497 | 6.160783 |
| 2459823 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.480756 | 0.165569 | 0.014150 | 0.387050 | 1.070376 | 0.467803 | 1.214390 | -0.352369 | 0.7720 | 0.6557 | 0.4488 | 2.635870 | 2.880892 |
| 2459822 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -0.331109 | 0.011612 | -0.290589 | 0.835753 | -0.055145 | 0.301803 | 0.247542 | 0.129258 | 0.8119 | 0.6315 | 0.5018 | 2.160582 | 1.696510 |
| 2459821 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 1.166909 | 0.582848 | -0.222628 | 0.514934 | 0.738653 | 0.961450 | -0.065199 | 0.098312 | 0.7984 | 0.6341 | 0.5034 | 2.226005 | 1.821203 |
| 2459820 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 1.60% | 0.683383 | -0.242211 | -0.383595 | 0.710867 | 0.055985 | 0.356574 | 1.703636 | -0.287105 | 0.7813 | 0.6883 | 0.4150 | 2.409509 | 2.019330 |
| 2459817 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.734611 | 0.185338 | -0.269373 | 0.471249 | -0.041378 | 0.417927 | 1.713534 | 1.167116 | 0.8040 | 0.6614 | 0.5016 | 2.352821 | 1.910558 |
| 2459816 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.237146 | -0.128159 | 0.710927 | 0.935387 | 0.245724 | 0.647251 | 1.839309 | -0.506671 | 0.8417 | 0.6066 | 0.5782 | 2.097630 | 1.581521 |
| 2459815 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.858994 | 0.006053 | 0.302605 | 0.551833 | -0.311206 | 0.669486 | 1.741462 | -0.333954 | 0.7951 | 0.6706 | 0.5097 | 2.257748 | 1.977867 |
| 2459814 | digital_ok | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.370723 | -1.137235 | -0.561728 | 0.125706 | -0.939858 | -0.805049 | 0.710805 | -0.819238 | 0.7905 | 0.6903 | 0.4237 | 2.845528 | 2.253176 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | ee Temporal Variability | 0.617059 | -0.971000 | -0.059707 | -0.515830 | -0.064717 | -1.148509 | 0.617059 | -1.059628 | -0.153974 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | ee Power | 0.868488 | -0.333441 | -1.336173 | 0.868488 | -0.568646 | 0.573825 | -0.268417 | -0.238041 | -0.833286 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | ee Temporal Discontinuties | 0.664242 | -1.012410 | 0.330814 | -0.924981 | -0.014710 | -1.377093 | 0.439540 | -1.382901 | 0.664242 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | ee Temporal Variability | 2.083557 | -0.953217 | -0.477631 | 0.643951 | 0.516115 | 2.083557 | -0.056970 | 1.110164 | 0.677131 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | nn Power | 1.195869 | -1.317854 | -1.078625 | 1.195869 | -0.569131 | 0.336879 | -0.893498 | -0.960001 | -0.384383 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | nn Power | 1.442299 | 0.048975 | -0.616074 | 0.342707 | 1.442299 | 0.464027 | 0.260036 | 0.006005 | 0.044363 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | ee Temporal Variability | 5.619375 | -0.077259 | 1.063557 | 0.505100 | 1.391335 | 5.619375 | -1.189353 | 2.009804 | 2.019914 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | nn Temporal Discontinuties | 10738.583623 | 233.239785 | 638.478181 | 129.755054 | 248.266629 | 1685.360470 | 4190.869258 | 3873.667600 | 10738.583623 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | nn Temporal Variability | 2.546842 | 0.108064 | -0.847477 | 0.154366 | 0.929016 | 2.546842 | 0.698629 | 0.949913 | 1.436278 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | ee Shape | 1.014209 | -0.635973 | 1.014209 | 0.394995 | -0.001290 | 0.062469 | 0.275001 | -0.274101 | 0.769355 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | nn Temporal Discontinuties | 41.729002 | -1.113363 | -1.050764 | -0.932414 | -1.135698 | 10.528144 | 13.643366 | 41.729002 | 41.343540 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | nn Temporal Discontinuties | 44.470719 | 0.622250 | 1.211034 | -0.258315 | -0.438841 | 14.736795 | 16.100960 | 44.470719 | 42.717252 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | ee Temporal Discontinuties | 0.793736 | -0.004625 | -0.512906 | -0.110757 | 0.142352 | -0.911517 | -0.628563 | 0.793736 | -0.719936 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | nn Temporal Variability | 1.554546 | -0.598583 | 0.068324 | 0.771229 | 0.156007 | -0.055063 | 1.554546 | 1.043641 | 0.861280 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | ee Temporal Discontinuties | 2.891889 | -0.615372 | 0.752784 | 0.787924 | 0.027876 | -0.275879 | 0.650322 | -0.269991 | 2.891889 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | ee Temporal Variability | 44.329740 | 0.658878 | 0.108232 | 2.038865 | 0.607006 | 44.329740 | -0.232618 | 6.968506 | -1.186698 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | ee Temporal Discontinuties | 2.088135 | 0.049882 | 0.161540 | 0.355315 | -0.124282 | 0.461673 | -0.113056 | -0.592594 | 2.088135 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | ee Temporal Discontinuties | 7.712168 | -0.132668 | 0.307998 | 0.589347 | 0.486581 | 6.249628 | 6.144993 | 5.727910 | 7.712168 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | ee Temporal Discontinuties | 6.845240 | 0.131042 | -0.415589 | 0.747069 | 0.719033 | 4.702924 | 4.225747 | 6.845240 | 5.561031 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | ee Temporal Discontinuties | 1.214390 | 0.165569 | 0.480756 | 0.387050 | 0.014150 | 0.467803 | 1.070376 | -0.352369 | 1.214390 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | nn Power | 0.835753 | -0.331109 | 0.011612 | -0.290589 | 0.835753 | -0.055145 | 0.301803 | 0.247542 | 0.129258 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | ee Shape | 1.166909 | 0.582848 | 1.166909 | 0.514934 | -0.222628 | 0.961450 | 0.738653 | 0.098312 | -0.065199 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | ee Temporal Discontinuties | 1.703636 | 0.683383 | -0.242211 | -0.383595 | 0.710867 | 0.055985 | 0.356574 | 1.703636 | -0.287105 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | ee Temporal Discontinuties | 1.713534 | 0.734611 | 0.185338 | -0.269373 | 0.471249 | -0.041378 | 0.417927 | 1.713534 | 1.167116 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | ee Temporal Discontinuties | 1.839309 | -0.128159 | 0.237146 | 0.935387 | 0.710927 | 0.647251 | 0.245724 | -0.506671 | 1.839309 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | ee Temporal Discontinuties | 1.741462 | 0.006053 | 0.858994 | 0.551833 | 0.302605 | 0.669486 | -0.311206 | -0.333954 | 1.741462 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | N14 | digital_ok | ee Temporal Discontinuties | 0.710805 | -1.137235 | 0.370723 | 0.125706 | -0.561728 | -0.805049 | -0.939858 | -0.819238 | 0.710805 |